Understanding risk-taking behavior of groups: A “decision analysis” perspective

Past research has extensively investigated the effect of media, especially focusing on how anonymity increases risk-related behaviors of groups when using computer-mediated communication (CMC). This study extends prior research by examining the differences in group risk-taking behaviors between face-to-face groups and completely non-anonymous CMC groups (i.e., groups working in a fully identified, synchronous CMC environment similar to popular instant messaging systems utilized widely within organizations). Drawing on the “decision analysis” perspective, a key framework for understanding organizational decision-making, the study also examines the effects of the firm's risk preferences as well as the type of information distribution among group members (i.e., full information known to all group members versus partial information know by only some of the members) on the groups' risk-taking behaviors. Results from a laboratory experiment using student subjects found no differences in risk-taking behaviors between CMC and face-to-face groups; additionally, no differences were found related to how information was distributed among group members. A significant effect was found, however, for the risk preference of the firm, showing that risk-neutral firms influenced groups to make riskier decisions than groups from risk-averse firms. Finally, groups within risk-neutral firms receiving partial information made riskier decisions than groups receiving full information. The implications of these results for future research and practice are examined.     

Empirical analysis of risk-taking behavior in IT platform migration decisions

Information technology (IT) platform migration incurs a great deal of risk because of the massive rewriting of legacy systems and a wide range of new technology adoptions. Therefore IT platform decisions need to be made in a mindful manner because of the high degree of investment risk. However, more aggressive migrations, compared to the global trend, to an open system have been observed in Korean organizations. This phenomenon defies the common understanding of IT investment risk and encourages the investigation of the elusive characteristics underlying IT investment decisions. The effect of IT decision-makers’ perceptions and risk propensity on their IT platform migration decisions is examined in the context of risky decisions. In addition to risk perception, decision-makers’ risk propensity also has a significant effect on IT investment decisions. By observing the monarchical IT governance and IT-biased careers of decision-makers, some implications of large-scale IT investment decisions are derived.     

Understanding consumer acceptance of mobile payment services: An empirical analysis

Mobile technology has become increasingly common in today’s everyday life. However, mobile payment is surprisingly not among the frequently used mobile services, although technologically advanced solutions exist. Apparently, there is still a lack of acceptance of mobile payment services among consumers. The conceptual model developed and tested in this research thus focuses on factors determining consumers’ acceptance of mobile payment services. The empirical results show particularly strong support for the effects of compatibility, individual mobility, and subjective norm. Our study offers several implications for managers in regards to marketing mobile payment solutions to increase consumers’ intention to use these services.     

Understanding the behavior of compiler optimizations

Compiler optimizations are difficult to implement and add complexity to a compiler. For this reason, compiler writers are selective about implementing them: they implement only the ones that they believe will be beneficial. To support compiler writers in this, we describe a method for measuring the cost and benefits of compiler optimizations, both individually and in synergy with other optimizations. We demonstrate our method by presenting results for the optimizations implemented in the Jikes Research Virtual Machine on the PowerPC and IA32 platforms. Copyright © 2006 John Wiley & Sons, Ltd.     

Understanding and predicting individual retweeting behavior: Receiver perspectives

Individual retweeting behavior plays a fundamental role in information propagation on social media. While researchers have demonstrated the benefits of individual retweeting behavior to online retailers, a largely uninvestigated issue is the mechanism of individual retweeting behavior. Drawing on the social communication theory, we develop an integrated conceptual framework to investigate this issue. An analysis of a panel dataset, which consists of 440 valid users from Twitter.com across six years, indicates that all the major components of a social communication process have significant impacts on individual retweeting decision: (1) information source (e.g., source trustworthiness), (2) information richness of the stimuli (3) information receiver's topical preference, (4) relationship between the source and the receiver (e.g., social tie strength), and (5) contextual factors (e.g., bandwagon effect). Among them, social tie strength and topical relevance with the receiver are the most important factors, followed by information richness of the stimuli and bandwagon effect. However, information source-related factors have trivial impacts on individual retweeting decision. We reevaluate the ranking using multiple feature selection methods and confirm the ranking result. Prediction performance is carried out between the salient features and the full feature set. Finally, we discuss important implications for practice and future research directions.     

Bidding behavior in dynamic auction settings: An empirical analysis of eBay

We study the impact of dynamic environment of eBay auctions on bidding behavior. Due to high-speed Internet and practically costless search possibilities, bidding behavior is no longer a function of characteristics of a single auction but depends on auctions running simultaneously, completed auctions, available Buy-It-Now prices as well as various outside options. We study how this dynamic market affects a bidder’s choice of participating in an auction or leaving eBay for an outside alternative. We analyze Texas Instruments (TI-83) Graphing Calculator auctions presented on eBay. We estimate a random-effects probit model to study bidders’ probability of staying in eBay, while controlling for unobservable individual-specific heterogeneity. Our main result shows that Market Tightness – the ratio of bidders to sellers – has a negative and significant effect on bidders’ decision to remain in eBay. Moreover, variables containing information from other eBay auctions significantly affect bidders’ participation decision, thus emphasizing the importance of the dynamic, multi-auction environment in eBay marketplace for potential buyers.     

An analysis of activity scheduling behavior of airport travelers

A nested logit model is presented that can be used to predict the activity pattern of travelers inside an airport based on their socio-demographical characteristics (e.g. gender, age), group size, and travel related information (e.g. number of bags, airport size, and total available time). The availability of such a model enhances representation of the behavior dynamics when simulating airport pedestrian traffic. An internet-based revealed preference survey was used to collect data from persons that visit airports including both travelers and non-travelers. The survey focused on the agenda of subjects’ most recent airport trip, the frequency and attitude concerning certain types of activities they performed inside the airport, and the socio-demographic characteristics of each respondent. Three possible nested logit model structures are analyzed and one has been identified as a plausible and statistically acceptable nested structure. Also, the empirical results demonstrate the applicability of a nested logit model for use in identifying travelers’ activity patterns in an airport.     

Understanding human behavior from motion imagery

Computer vision is gradually making the transition from image understanding to video understanding. This is due to the enormous success in analyzing sequences of images that has been achieved in recent years. The main shift in the paradigm has been from recognition followed by reconstruction (shape from X) to motion-based recognition. Since most videos are about people, this work has focused on the analysis of human motion. In this paper, I present my perspective on understanding human behavior. Automatically understanding human behavior from motion imagery involves extraction of relevant visual information from a video sequence, representation of that information in a suitable form, and interpretation of visual information for the purpose of recognition and learning about human behavior. Significant progress has been made in human tracking over the last few years. As compared with tracking, not much progress has been made in understanding human behavior, and the issue of representation has largely been ignored. I present my opinion on possible reasons and hurdles for slower progress in understanding human behavior, briefly present our work in tracking, representation, and recognition, and comment on the next steps in all three areas.Published online: 28 August 2003     

Understanding problem building: ergonomic work analysis

Abstract

The circumstances in which work is carried out in the real world are not usually taken into account in the modelling of work activities. The task is variable and often interrupted; the work teams are not always stable; feedback loops are numerous and complex making it difficult to diagnose disturbances. The theoretical models often overlook the physical aspects of the activity, for example fatigue, pain and danger, and the way these vary between individuals and within individuals at different times. The critical elements of the task are not always delimited according to the recommendations of the task. Operators have preconceptions about situations that are often useful but sometimes dangerous. Finally, each person reacts in terms of his personal culture. These elements are so important that it is necessary to single out a problem building phase that precedes and accompanies the more classic phase of problem solving. Ergonomic Work Analysis (EWA) is a tried and tested methodology which, as the result of studying behaviour in the work situation, provides an understanding of how the operator builds the problem, indicates any obstacles in the path of this activity, and enables the obstacles to be removed through ergonomic action.     

Towards software process patterns: An empirical analysis of the behavior of student teams

Traditional software engineering processes are composed of practices defined by roles, activities and artifacts. Software developers have their own understanding of practices and their own ways of implementing them, which could result in variations in software development practices. This paper presents an empirical study based on six teams of five students each, involving three different projects. Their process practices are monitored by time slips based on the effort expended on various process-related activities. This study introduces a new 3-pole graphical representation to represent the process patterns of effort expended on the various discipline activities. The purpose of this study is to quantify activity patterns in the actual process, which in turn demonstrates the variability of process performance. This empirical study provides three examples of patterns based on three empirical axes (engineering, coding and V&V). The idea behind this research is to make developers aware that there is wide variability in the actual process, and that process assessments might be weakly related to actual process activities. This study suggests that in-process monitoring is required to control the process activities. In-process monitoring is likely to provide causal information between the actual process activities and the quality of the implemented components.     

Ethical behavior issues in software use: An analysis of public and private sectors

Ethical issues related to information systems are important to the information technology (IT) professionals. These issues are also significant for organizations and societies. Although considerable literature on IT and related ethical issues exists, a review of this literature has found little empirical research on ethical practices within the government and private sector organizations. Therefore, the objective of this paper is to draw inferences regarding such practices currently in these sectors. The research results indicate a significant correlation between the code of ethics and the attitude of professionals towards the unethical use of software in government and private sector organizations. These also indicate significant differences in government and private sectors.     

Understanding the impact of business cases on IT investment decisions: An analysis of municipal e-government projects

This study synthesizes the extant literature to derive an integrative developmental framework for IT business cases that can be applied to diagnose the feasibility of technological investments. We then construct a theoretical model that postulates the impact of IT business case elements on the initial cost estimates of technological investments. Subsequently, our theoretical model is subjected to empirical validation through content analysis of IT business cases developed for municipal e-government projects. Findings indicate that the richness of the richness of business cases translates to more initial costs being identified in technological investments, thereby conserving resources for the organization through informed investment decisions.     

Understanding the behavior of mobile data services consumers

Due to rapid advances in the Internet and wireless technologies, a ubiquitous computing world is becoming a reality in the form of mobile computing. At the center of this phenomenon is mobile data services which arise from the convergence of advanced mobile communication technologies with data services. Despite the rapid growth in mobile data services, research into consumers’ usage behavior is scarce. This study attempts to identify and empirically assess the factors that drive consumers’ acceptance of mobile data services. A research model based on the decomposed theory of planned behavior and incorporating factors that represent personal needs and motivations in using mobile data services is presented. The model is tested via an online survey of 811 consumers of four categories of mobile data services (i.e., communications, information content, entertainment, and commercial transactions) associated with different usage contexts. We found that attitude, social influence, media influence, perceived mobility, and perceived monetary value influence consumers’ intention to continue usage of mobile data services. In addition, perceived ease of use, perceived usefulness, and perceived enjoyment influence attitude toward continued usage of mobile data services. Finally, separate analysis of the different categories of mobile data services highlights the influence of individual usage context on consumers’ behavior.

Understanding user behavior of virtual personal assistant devices

Information Systems and e-Business Management - With the development of artificial intelligence technology, the market for virtual personal assistant (VPA) devices is emerging as a new battleground...     

Understanding software project risk: a cluster analysis

Understanding software project risk can help in reducing the incidence of failure. Building on prior work, software project risk was conceptualized along six dimensions. A questionnaire was built and 507 software project managers were surveyed. A cluster analysis was then performed to identify aspects of low, medium, and high risk projects. An examination of risk dimensions across the levels revealed that even low risk projects have a high level of complexity risk. For high risk projects, the risks associated with requirements, planning and control, and the organization become more obvious. The influence of project scope, sourcing practices, and strategic orientation on project risk dimensions was also examined. Results suggested that project scope affects all dimensions of risk, whereas sourcing practices and strategic orientation had a more limited impact. A conceptual model of project risk and performance was presented.     

An analysis of catastrophic behavior as a cause of human error

It is shown that some catastrophic aspect is seen in human behavior. The notions of localization and experience sequence are utilized in order to provide the human operator a good circumstance in man-machine communication in the sense of human-oriented information processing.     

Understanding online knowledge sharing: An interpersonal relationship perspective

The unique features and capabilities of online learning are built on the ability to connect to a wider range of learning resources and peer learners that benefit individual learners, such as through discussion forums, collaborative learning, and community building. The success of online learning thus depends on the participation, engagement, and social interaction of peer learners, which leads to knowledge sharing. Thus, without frequent and persistent interaction, it is doubtful whether knowledge sharing can take place in online learning. This study argues that theories about the development and maintenance of social relationships provide a theoretical foundation for understanding the motivation to engage in online knowledge sharing behavior. An Online Knowledge Sharing Model (OKSM) is proposed and empirically tested among undergraduate students using an online learning environment. The model introduces two new constructs – Perceived Online Attachment Motivation (POAM) and Perceived Online Relationship Commitment (PORC), which together explained 71 percent of the variance observed in self-reported online knowledge sharing behavior. The findings provide some explanations for the motivation to share knowledge, and have several implications for the design of the features and capabilities of online learning environments.     

Understanding the role of gender in bloggers' switching behavior

Blog has become an increasingly popular form of social networking technology in recent years. Many world famous web sites (e.g., Microsoft, Yahoo!, and Google) are providing blog services on the Internet. Bloggers, therefore, can easily establish and maintain their blogs through such services. However, research on blogs' adoption and diffusion is rare. In this study, we attempt to study bloggers' post-adoption behaviors. We examine bloggers' intention to switch their blog services by building upon studies in the marketing and IS literature. Particularly, the role of gender is explored and an online survey is conducted to test the research model. Findings confirm that bloggers' intention to switch their blog services is strongly associated with three factors: satisfaction, sunk costs, and attractive alternatives. Meanwhile, moderating effects are found in gender, but not in sunk costs. Implications for both researchers and practitioners are provided.